Detecting random-effects model misspecification via coarsened data
Detecting random-effects model misspecification via coarsened data
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DOI：
10.1016/j.csda.2010.06.012
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发表时间：
2011-01-01
影响因子：
1.8
通讯作者：
Huang, Xianzheng
中科院分区：
文献类型：
--
作者：
Huang, Xianzheng
混合效应模型在广泛的应用中为统计推断提供了一个合适的框架。这类模型的似然推断的有效性通常取决于对随机效应的假设。我们开发了诊断工具，用于在丰富的混合效果模型中检测随机效果模型的错误指定。通过对大豆生长数据的模拟和应用，说明了这些方法的有效性。(C)2010爱思唯尔B.V.保留所有权利。
Mixed effects models provide a suitable framework for statistical inference in a wide range of applications. The validity of likelihood inference for this class of models usually depends on the assumptions on random effects. We develop diagnostic tools for detecting random-effects model misspecification in a rich class of mixed effects models. These methods are illustrated via simulation and application to soybean growth data. (C) 2010 Elsevier B.V. All rights reserved.